2014 The Third International Conference on Agro-Geoinformatics最新文献

{"title":"GCDViewer: An online data query, visualization and analysis system for global climatic data","authors":"Hao Xu, Yuqi Bai","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910601","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910601","url":null,"abstract":"Climatic data are critical in supporting of the agricultural and ecological studies. Traditionally, the way of managing and accessing to the data is locally fulfilled. To demonstrate the value of cyberinfrastructure and to ease the access to the data, this paper presented a case study of managing, visualizing and analyzing the global multi-station climatic data in a Web-based environment. This study took 1961-1990 global standard climate normals data published by World Meteorological Organization as an exemplar. It proposed a relational data model to store the climate normals data. It utilized SQL Server to store and manage the data as Web-accessible data services, ArcGIS Server as GIS Server to enable on-demand accessuser operation map and presented an ASP.Net-based web portal for scientists to fulfill data search, data visualization and data analysis functions. The system design and the implementation details of this prototype are introduced. This case study clearly shows that the unstructured climatic text data could be transformed to be structured database, which further enables the on-demand data sharing, search, retrieval and online analysis functions. GCDViewer's design principle and system architecture could apply to many other types of scientific data. It clearly shows the advantage of building cyberinfrastructure to improve the overall research efficiency.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leaf recognition and segmentation by using depth image","authors":"Xiaowei Shao, Yun Shi, Wenbin Wu, P. Yang, Zhongxin Chen, R. Shibasaki","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910605","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910605","url":null,"abstract":"Measuring the geometric structural traits of plants, especially the shape of leaves, plays an important role in the agricultural science. However, most existing techniques and systems have limited overall performance in accuracy, efficiency and descriptive ability, which is insufficient for the requirements in many real applications. In this study, a new kind of sensing device, the Kinect depth sensor which measures the real distance to objects directly and is able to capture high-resolution depth images, is exploited for the automatic recognition and extraction of leaves. The pixels of the depth image are converted into a set of 3D points and transformed into a standard coordinate system after ground calibration. Leaves are extracted based on the height information and a hierarchical clustering algorithm, which combines the density-based spatial clustering algorithm and the mean-shift algorithm, is proposed for the automatic segmentation of leaves. Experimental result shows the effectiveness of our proposed method.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131085213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variation of drought frequency of winter wheat in Hebei Province","authors":"Liu Xianfeng, Zhu Xiufang, Pan Yaozhong, Zhao Anzhou, Lin Muyi, Li Lin","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910619","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910619","url":null,"abstract":"Based on the daily meteorological data of 23 meteorological stations during 1960-2013 across the study area and its surroundings, the spatiotemporal variation of drought frequency of winter wheat is investigated by using the crop water deficit index (CWDI). The results show that during the entire growth period, the sequence of drought frequency is extreme drought > severe drought > moderate drought > light drought. The frequencies of moderate and severe droughts presented increasing trends after 1995, whereas light and extreme droughts showed decreasing trends during the same period. In addition, the drought frequency decreased from seeding to maturation, and the fluctuation of drought frequency tended to intense with an increase in the level of drought, especially during green-up to jointing. During that period, the frequency of extreme drought presented a decreasing trend after 1995, whereas an increasing trend was detected in severe drought conditions. Moreover, significant spatial differences in the trend of drought frequency were detected. For example, severe drought presented an increasing trend in the northwest parts of the study area and a decreasing trend in the east. Increasing and decreasing trends in extreme drought were detected in the northern and southern regions of the study area, respectively. Further, the drought frequency was characterized as a decreasing trend during seeding to overwinter. The frequencies of light, moderate, and severe droughts presented decreasing trends, whereas extreme drought showed an increasing trend during green-up to jointing. On the contrary, a reverse trend was detected during tasselling to maturation.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal design on samples layout of spatial sampling schemes for estimating winter wheat planting acreage","authors":"Wang Di, Chen Zhongxin, Zhou Qingbo, An Yi","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910574","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910574","url":null,"abstract":"Sample layout is one of key factors in spatial sampling schemes for estimating crop planting acreage. It plays an important role that optimizing sample layout for improving the representativeness of samples versus population and the accuracy of population extrapolation, decreasing the cost of survey sampling. In this study, focusing on the problem that the samples layout design is not reasonable (e.g. samples units are not all independent of each other, when simple random sampling method is used to design samples layout; sampling intervals are not defined reasonably, when systematic sampling method is used to distribute samples in space), we tried to propose a optimal scheme of samples layout to improve the spatial sampling efficiency. Mengcheng County in Anhui Province, China was chosen as the study area, winter wheat planting acreage as the study object, and square girds as the shape of sampling units. Geostatistics, “3S” technology (Remote Sensing, Geographic Information Systems and Global Positioning systems) and traditional sampling methods are used in this paper. Firstly, 8 kinds of sampling unit sizes are formulated, and then the study area is subdivided by the sampling units with the 8 kinds of sizes to construct the sampling frame. The winter wheat acreages in all sampling units are calculated based on the spatial distribution data of winter wheat in 2009 and 2010(derived by ALOS AVNIR-2 and Landsat5 TM image, respectively); Secondly, in order to build the Variogram theoretical model of winter wheat acreage proportion within one sampling unit (WPS), simple random sampling method is used to draw the initial samples. Spatial correlation and variability of sampling units are analyzed, and spatial correlation threshold is quantitatively determined by the Variogram model; Thirdly, the equal interval pattern (sampling intervals are the same in vertical and horizontal directions, and spatial correlation threshold of samples is chosen as the sampling interval) is used to reasonably formulate the samples layout; Finally, the extrapolation accuracy, stability and sampling cost are estimated based on the samples after the layout are reasonably designed. In order to evaluate the design effect of samples layout, relative error, coefficient of variation (CV) and sampling size are selected as the indices, and simple random sampling method as the control treatment. The experimental results demonstrate that, the variability of WPS increases with sampling unit size increasing. CV of WPS varies from 32.75% to 43.46% under 8 sampling unit size levels; Spatial correlation thresholds of WPS increase with sampling unit size increasing; The relative error and CV of population extrapolation that samples layout is optimized are obviously less than those of simple random sampling method, when sampling unit size is small (500m×500m~2000m×2000m); Although the relative error and CV are not reduced after optimized design of sample layout, they occur an obvious decreas","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133687780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting of powdery mildew disease with multi-sources of remote sensing information","authors":"Jingcheng Zhang, Lin Yuan, Chenwei Nie, L. Wei, Guijun Yang","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910569","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910569","url":null,"abstract":"Powdery mildew (PM) is a typical disease in winter wheat which causes severe yield loss in China. To control the disease effectively, it is important to develop a disease forecasting model at a regional scale. In this study, the remotely sensed data that reflect crop vigor and habitat traits were adopted as candidate inputs in model development, including various vegetation indices, land surface temperature and plant's drought index. Based upon a correlation analysis, a total of 9 remotely sensed variables at specific growing stages that had significant response to PM were identified as explanatory variables. To assess the ground truth of PM occurrence, a field campaign was conducted in suburban area of Beijing in 2010. According to the remote sensing data and corresponding ground truth data, the PM forecasting model was established in terms of the logistic regression analysis. The validation result showed that the disease risk map could reflect the general spatial distribution pattern of PM occurrence in the study area, with an overall accuracy of 72%. To facilitate the disease control practices, the map of disease probability was converted to a binary map (presence/absence) using a thresholding method. The potential of remote sensing information in PM forecasting is illustrated in this study.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129539519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the crop acreage at county level on the North China Plain using an adapted regession estimator method","authors":"Jia Liu, Zhongxin Chen, Limin Wang, Xiao Wang, Qinghan Dong, J. Gallego","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910679","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910679","url":null,"abstract":"Image classifications including sub pixel analysis are often used to estimate directly the crop acreage, while ground data collected during field surveys play a secondary role. This type of crop area assessment using image classifications often leads to a biased estimation due to non-representative selection of training data and subjective a-priori knowledge. Instead regression estimator approach combining remote sensing information with a rigorous ground sampling can result in an accurate assessment of crop acreage. In this study to produce the crop statistics, the area frame sampling approach is adapted to the strip-like cropping pattern on the North China Plain. Remote sensing information is used to perform a cost-efficient stratification from which no-agricultural areas are excluded from ground survey. This information is also included in a later stage as an auxiliary estimator in regression analysis. The results showed that the integration of remote sensing information as an auxiliary estimator can improve the confidence of estimation by reducing the variance of the estimates.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123090495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Rough Set theory to support OWSs semantic reasoning","authors":"L. Miao, Donglai Jiao, Jie Xu, Ya Zhou, Wenchao Cheng","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910595","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910595","url":null,"abstract":"This paper reports our research on utilizing discernibility matrix method of Rough Set (RS) theory to improve the OGC web services (OWSs) semantic search. Based on RS theory, we build a description vocabulary reduction model involving attributes and spatial relation for OWSs and implement knowledge reduction on Geographic Information Service (GISe) instance-database. Also, the semantic reasoning framework is generated to construct the prototype using the reduction model, which implements semantic search, reasoning and semantic similarity evaluation regarding to spatial relation. We expect this research to support GISe semantic search in an intelligent manner.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of farmland quality with the integration of GIS and RS technology at village scale","authors":"X. Gu, Yanchang Wang, Xiaodong Yang, Yanling Chen","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910578","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910578","url":null,"abstract":"Mastering farmland quality is significant for achieving soil fertility management and promoting sustainable development of farmland resource. Evaluating the farmland quality is a complex problem, because many influencing factors are interrelated and interaction. The Yanba village in Chongqing city in China was chosen as study area. According to the existing index system of agricultural department, the study screened the critical influencing factors, including natural factors and social factors, which could be mapped by spatial analysis technology at village scale. The Delphi method was used to determine the weight of each influencing factors. Taking the farmland parcels as basic units, the comprehensive evaluation model for farmland quality at village scale was developed, through which the s farmland quality in the Yanba village was mapped and analyzed. By the method of density slice and threshold adjustment, the farmland quality in the study area was divided into four levels. Results showed that the spatial distribution of farmland with different quality levels derived from the developed model was consistent with the data of the agricultural department in the study area. This finding indicated that the comprehensive evaluation model with multi-source spatial information could effectively map the farmland with different quality levels at village scale.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115746072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenology detection of winter wheat in the Yellow River delta using MODIS NDVI time-series data","authors":"Gaohuan Liu, Chong Huang, Qingsheng Liu, Lin Chu","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910664","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910664","url":null,"abstract":"Phenology detection has a significant impact on monitoring crop growth and crop yield estimation. Due to short time terrestrial formation, shallow buried depth and high salinity groundwater, soil salinization is serious and has negative influence on crop growth stages in the Yellow River delta. Traditional method for phenology detection using situ data is far more costly and time consuming, moreover, hard to available for all the fields. Soil salinization differs from place to place. The phenological stage varies from low salinization area to high salinization area. MODIS data provides the possibility for regional dynamic monitoring in a timely and accurate way due to its repeated acquisition and broad area coverage. Aim of this study is to detect major phenology and analyze the spatial distribution characters of phenology affected by soil salinization for winter wheat in the Yellow River delta region based on the MODIS NDVI time-series data. Savitzky-Golay filter procedure was selected to denoise. Decision tree method was used to classify winter wheat from other crops and natural vegetation before phenology detection. Phenology was specified by using defined dynamic threshold method. The phenology of green-up stage, heading stage, and harvesting stage was detected. This study concludes that green-up date generally came in early March, headed in early May and harvested in early June. The overall average green-up date occurred in March 4, heading date in May 7 and harvesting date in June 2. The detection result was consistent with the ground observations result on the whole. The spatial distribution of phenology showed a gradual postponement from inland to coast. Heading date and harvesting stage inland might be about 3 days in advance than those near the sea. Green-up stage inland might be about 6 to 10 days earlier than near the coast. Green-up stage was significantly influenced by soil salinity comparing to heading date and harvesting date. Method proposed in this paper can be used in phenology detection for winter wheat in Yellow River delta region, which has important guiding significance for crop condition evaluation and phenology detection in other coastal salinization area.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117164191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Object oriented extraction of reserve resources area for cultivated land using RapidEye image data","authors":"Yanmin Yao, Haiqing Si, Deying Wang","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910671","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910671","url":null,"abstract":"Identify the amount and spatial distribution of reserve cultivatable land resources is the basis for its development to increase crop planting areas. Taking Jiaxiang county of Shandong Province of China as a case study, this paper conducted image segmentation and merge based on RapidEye image data (5m spatial resolution) after data preprocessing. Then, object-oriented approach was used to classify land use information and the reserve resources of arable land were extracted from them. The results showed: (1) 30% and 80% of the scale level and merge level for image segmentation and merge were chosen for getting better results of independent polygon division based on object-oriented approach; (2) Comparing with K near value method (KNN) and principal component analysis method (PCA), support vector machine (SVM) method had 78% of the highest overall accuracy for the supervised classification; (3) The overall land use classification accuracy was 90.4% verified by field survey data and 1:10000 land use map in 2011, Kappa coefficient was 0.8784. Therefore, using high spatial resolution image can improve the classification accuracy for the reserve cultivatable land resources; (4) Bare land, wild grassland, mudflats and reed land were the main reserve resources of cultivated land for the study region. The area was 2640 ha occupying 2.95% of the total land area only. The area of bare land and wild grassland accounts for 61% and 35% of the reserve cultivatable land resources. Thin soil thickness and lack of irrigation facilities were major limit factors for its development to cropland.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122734368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}